On the Evaluation of an Entropy-Based Spectrum Sensing Strategy Applied to Cognitive Radio Networks

Prieto, Guillermo; Andrade, Ángel G.; Martinez, Daniela M.; Galaviz, Guillermo

doi:10.1109/access.2018.2876499

Cited by 14 publications

(10 citation statements)

References 18 publications

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“…The classification metrics adopted on this paper are adopted on the basis of two considerations. The first consideration is that they are commonly used in literature to evaluate the performance of the spectrum sensing algorithm [ 4 , 6 , 15 ]. The second consideration is they are derived from the binary hypothesis test, which is used to find out the presence of the PU or incumbent signal:

is the noise in the absence of a PU/incumbent signal and

indicates the presence of a PU/incumbent signal as defined in the following equations:

where

is the PU/incumbent signal,

is the noise and i = 0, 1, 2, … , N , is the sample size under analysis.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Therefore, the entropy is calculated in the frequency domain. In fact, this approach has been quite popular in literature since it has been applied to various spectrum sensing scenarios and cognitive networks typologies (e.g., cooperative sensing, in combination with cyclostationary detectors) like [ 14 , 15 ]. Previous works adopted the Shannon entropy measure in the time domain like [ 16 ] where a superior detection performance in comparison to ED and cyclostationary detection techniques is also demonstrated.…”

Section: Related Workmentioning

confidence: 99%

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Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

Baldini

Chareau

Bonavitacola

2021

Entropy

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Spectrum sensing is an important function in radio frequency spectrum management and cognitive radio networks. Spectrum sensing is used by one wireless system (e.g., a secondary user) to detect the presence of a wireless service with higher priority (e.g., a primary user) with which it has to coexist in the radio frequency spectrum. If the wireless signal is detected, the second user system releases the given frequency to maintain the principle of not interfering. This paper proposes a machine learning implementation of spectrum sensing using the entropy measure as a feature vector. In the training phase, the information about the activity of the wireless service with higher priority is gathered, and the model is formed. In the classification phase, the wireless system compares the current sensing report to the created model to calculate the posterior probability and classify the sensing report into either the presence or absence of wireless service with higher priority. This paper proposes the novel application of the Fluctuation Dispersion Entropy (FDE) measure recently introduced in the research community as a feature vector to build the model and implement the classification. An improved implementation of the FDE (IFDE) is used to enhance the robustness to noise. IFDE is further enhanced with an adaptive method (AIFDE) to automatically select the hyper-parameter introduced in IFDE. Then, this paper combines the machine learning approach with the entropy measure approach, which are both recent developments in spectrum sensing research. The approach is compared to similar approaches in literature and the classical energy detection method using a generated radar signal data set with different conditions of SNR(dB) and fading conditions. The results show that the proposed approach is able to outperform the approaches from literature based on other entropy measures or the Energy Detector (ED) in a consistent way across different levels of SNR and fading conditions.

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is the noise in the absence of a PU/incumbent signal and

indicates the presence of a PU/incumbent signal as defined in the following equations:

where

is the PU/incumbent signal,

is the noise and i = 0, 1, 2, … , N , is the sample size under analysis.…”

Section: Materials and Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

Baldini

Chareau

Bonavitacola

2021

Entropy

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“…It has a low computational complexity and a fast sensing time. However, noise uncertainty, which is the random or unpredictable and unavoidable variation of noise in any wireless communication system, severely reduces ED performance, especially when SNR is low [6]. Many methods have been proposed to address the challenges of ED.…”

Section: Introductionmentioning

confidence: 99%

Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection

Usman

Singh

Rajkumar

2022

Wireless Power Transfer

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The radio spectrum is one of the world’s most highly regulated and limited natural resources. The number of wireless devices has increased dramatically in recent years, resulting in a scarcity of available radio spectrum due to static spectrum allocation. However, many studies on static allocation show that the licensed spectrum bands are underutilized. Cognitive radio has been considered as a viable solution to the issues of spectrum scarcity and underutilization. Spectrum sensing is an important part in cognitive radio for detecting spectrum holes. To detect the availability or unavailability of primary user signals, many spectrum sensing techniques such as matched filter detection, cyclostationary feature detection, and energy detection have been developed. Energy detection has gained significant attention from researchers because of its ease of implementation, fast sensing time, and low computational complexity. Conventional detectors’ performance degrades rapidly at low SNR due to their sensitivity to the uncertainty of noise. To mitigate noise uncertainty, Shannon, Tsallis, Kapur, and Renyi entropy-based detection has been used in this study, and their performances are compared to choose the best performer. According to the comparison results, the Renyi entropy outperforms other entropy methods. In this study, two-stage spectrum sensing is proposed using energy detection as the coarse stage and Renyi entropy-based detection as the fine stage to improve the performance of single-stage detection techniques. Furthermore, the performance comparison among conventional energy detection, entropy-based detection, and the proposed two-stage techniques over AWGN channel are performed. The parameters such as probability of detection, false alarm probability, miss-detection probability, and receiver operating characteristics curve are used to evaluate the performance of spectrum sensing techniques. It has been shown that the proposed two-stage sensing technique outperforms single-stage energy detection and Renyi entropy-based detection by 11 dB and 1 dB, respectively.

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“…As the uncertainty of noise is higher than that of the signal, the entropy of the noise is higher than that of the signal, which is the basis of using entropy to detect signals. Information entropy has been successfully applied to signal detection [ 10 , 11 , 12 ]. The main entropy detection methods can be classified into two categories: the time domain and the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

“…So [ 18 ] used the conditional entropy of the spectrum magnitude to detect unauthorized user signals in cognitive radio networks. Guillermo et al [ 11 ] proposed an improved entropy estimation method based on Bartlett periodic spectrum. Ye et al [ 19 ] proposed a method based on the exponential entropy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio

Han

Wang

2021

Entropy

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In this paper, we present a novel blind signal detector based on the entropy of the power spectrum subband energy ratio (PSER), the detection performance of which is significantly better than that of the classical energy detector. This detector is a full power spectrum detection method, and does not require the noise variance or prior information about the signal to be detected. According to the analysis of the statistical characteristics of the power spectrum subband energy ratio, this paper proposes concepts such as interval probability, interval entropy, sample entropy, joint interval entropy, PSER entropy, and sample entropy variance. Based on the multinomial distribution, in this paper the formulas for calculating the PSER entropy and the variance of sample entropy in the case of pure noise are derived. Based on the mixture multinomial distribution, the formulas for calculating the PSER entropy and the variance of sample entropy in the case of the signals mixed with noise are also derived. Under the constant false alarm strategy, the detector based on the entropy of the power spectrum subband energy ratio is derived. The experimental results for the primary signal detection are consistent with the theoretical calculation results, which proves that the detection method is correct.

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On the Evaluation of an Entropy-Based Spectrum Sensing Strategy Applied to Cognitive Radio Networks

Cited by 14 publications

References 18 publications

Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection

A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio

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